K-means

K-means is an unsupervised, iterative clustering algorithm partitioning $n$ data points into $k$ clusters by minimizing the within-cluster sum of squared distances to the centroid (mean).

K-means is a centroid-based, unsupervised machine learning algorithm designed to partition $n$ observations into a pre-defined number ($k$) of non-overlapping clusters. The process is iterative: first, $k$ initial cluster centroids are randomly selected; second, every data point is assigned to the nearest centroid (typically via Euclidean distance); third, the centroid is recalculated as the mean of all points in the new cluster. This assignment and update cycle repeats until the centroids stabilize or a maximum iteration count is reached, effectively minimizing the Within-Cluster Sum of Squares (WCSS). This is a fast, efficient algorithm, commonly deployed for tasks like market segmentation and image compression.

https://en.wikipedia.org/wiki/K-means_clustering